kurtosis#
- scipy.stats.mstats.kurtosis(a, axis=0, fisher=True, bias=True)[source]#
Computes the kurtosis (Fisher or Pearson) of a dataset.
Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.
If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators
Use
kurtosistestto see if result is close enough to normal.- Parameters:
- aarray
data for which the kurtosis is calculated
- axisint or None, optional
Axis along which the kurtosis is calculated. Default is 0. If None, compute over the whole array a.
- fisherbool, optional
If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0).
- biasbool, optional
If False, then the calculations are corrected for statistical bias.
- Returns:
- kurtosisarray
The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s definition and 0 for Pearson’s definition.
Notes
For more details about
kurtosis, seescipy.stats.kurtosis.